
# Default libraries 
import pandas as pd
import numpy as np
#import matplotlib.pyplot as plt
#import project_tests as t
#import pickle

# Other imported libraries 
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
#import plotly.figure_factory as ff

# Importing metric scores
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score

#%matplotlib inline

df = pd.read_csv('data/user-item-interactions.csv')
df_content = pd.read_csv('data/articles_community.csv')
del df['Unnamed: 0']
del df_content['Unnamed: 0']

# Show df to get an idea of the data
df.head()


# Show df_content to get an idea of the data
df_content.head()


df.isnull().sum()['email']

17


nan_email_df = df.loc[df.email[df.email.isnull()].index]
nan_email_df


nan_email_df.groupby('article_id').count().sort_values(by=['title'], ascending=False)


df_data = df.dropna()

df_data.head()


# Observe that the email are the users and the article_id are the articles. 
user_article_df = df_data.groupby('email')['article_id'].count().reset_index(); 
user_article_df.head()


hist_fig = px.histogram(user_article_df['article_id'], 
                        nbins=50, 
                        title="Distribution of the number of article interactions per user", 
                        labels = {'value': "Number of articles per user"}, 
                        template= "plotly_dark"
                       )

hist_fig.update_layout(title_x=0.5, 
                       yaxis_title="Number of users", 
                       showlegend=False
                      )

hist_fig.show(config={'displaylogo': False})


# 50% of individuals interact with 3 number of articles or fewer.

median_val = user_article_df.describe().loc['50%']['article_id']
median_val

3.0


# The maximum number of user-article interactions by any 1 user is 364.

max_views_by_user = user_article_df.describe().loc['max']['article_id']
max_views_by_user

364.0


# Find and explore duplicate articles

# Get the article_id's that are duplicated.

duplicate_ids = df_content[df_content.article_id.duplicated()].article_id.unique()

# Dataframe of all duplicated articles in df_content

df_content[df_content.article_id.isin(duplicate_ids)].sort_values(by=['article_id'])


# Remove any rows that have the same article_id - only keep the first
df_content.drop_duplicates(subset=["article_id"], keep="first", inplace=True)
df_content.head()


# The number of unique articles that have at least one interaction
unique_articles = df.article_id.nunique() 
unique_articles

714


# The number of unique articles on the IBM platform
total_articles = df_content.article_id.nunique()
total_articles

1051


# The number of unique users
unique_users = df_data.email.nunique()
unique_users

5148


# The number of user-article interactions. Note that this includes unknown users, i.e., 
# missing data in the email column.
user_article_interactions = df.shape[0] 
user_article_interactions

45993


# The most viewed article in the dataset was viewed how many times?
most_viewed_article_df = df.groupby("article_id")['email'].count().sort_values(ascending=False).reset_index()[0:1]
most_viewed_article_df


# The most viewed article in the dataset as a string with one value following the decimal 
most_viewed_article_id = most_viewed_article_df.loc[0]['article_id']

# Get the title of the most viewd article
most_viewed_article_title = df[df.article_id == most_viewed_article_id].drop_duplicates(subset=['article_id'])['title'].values[0].capitalize()

# The most viewed article in the dataset was viewed how many times?
max_views = most_viewed_article_df.loc[0]['email']

print("The most viewed article is '{}', with article id {}. It had {} number of reads.".format(most_viewed_article_title,
                                                                                               most_viewed_article_id, 
                                                                                               max_views
                                                                                              ))

The most viewed article is 'Use deep learning for image classification', with article id 1429.0. It had 937.0 number of reads.


def email_mapper():
    """
    INPUT: 
        - None
    
    OUTPUT:
        - email_encoded - A Python list - A list of integers which are the user ids
    """
    coded_dict = dict()
    counter = 1
    email_encoded = []
    
    # For each email or user in df['email']
    for val in df['email']:
        
        # If the email or user is not in coded_dict
        if val not in coded_dict:
            
            # Update coded_dict by setting the key as val, 
            # i.e, user and its value as counter
            coded_dict[val] = counter
            
            # Increment counter by 1
            counter+=1
        
        # Add the value of the key, i.e., the user to the list email_encoded
        email_encoded.append(coded_dict[val])
        
        # Return email_encoded
    return email_encoded

email_encoded = email_mapper()
del df['email']
df['user_id'] = email_encoded

# show header
df.head()


def get_top_articles(n, df=df):
    '''
    INPUT:
    n - (int) the number of top articles to return
    df - (pandas dataframe) df as defined at the top of the notebook 
    
    OUTPUT:
    top_articles - (list) A list of titles of the the top 'n' article. 
    
    '''
    # Your code here
    
    # Get the first n most interracted article ids 
    
    top_n_article_ids = df['article_id'].value_counts().index[:n]
    
    # Get the titles of the articles with ids in top_n_article_ids
    
    top_article_titles = df[df['article_id'].isin(top_n_article_ids)]['title'].unique().tolist()
    
    # Return the top article titles from df (not df_content)
    
    return top_article_titles 


def get_top_article_ids(n, df=df):
    '''
    INPUT:
    n - (int) the number of top articles to return
    df - (pandas dataframe) df as defined at the top of the notebook 
    
    OUTPUT:
    top_articles - (list) A list of the id's of the top 'n' article titles 
    
    '''
    # Your code here
    
    top_article_ids = df['article_id'].value_counts().index[:n].tolist()
    
    # Return the top article ids
    
    return top_article_ids


print(get_top_articles(10))
print(get_top_article_ids(10))

['healthcare python streaming application demo', 'use deep learning for image classification', 'apache spark lab, part 1: basic concepts', 'predicting churn with the spss random tree algorithm', 'analyze energy consumption in buildings', 'visualize car data with brunel', 'use xgboost, scikit-learn & ibm watson machine learning apis', 'gosales transactions for logistic regression model', 'insights from new york car accident reports', 'finding optimal locations of new store using decision optimization']
[1429.0, 1330.0, 1431.0, 1427.0, 1364.0, 1314.0, 1293.0, 1170.0, 1162.0, 1304.0]


# create the user-article matrix with 1's and 0's

def create_user_item_matrix(df):
    '''
    INPUT:
    df - pandas dataframe with article_id, title, user_id columns
    
    OUTPUT:
    user_item - user item matrix 
    
    Description:
    Return a matrix with user ids as rows and article ids on the columns with 1 values where a user interacted with 
    an article and a 0 otherwise
    '''
    
    # Remove all duplicates in the columns article_id and user_id
    
    df = df.drop_duplicates(subset=['article_id', 'user_id'])
    
    # Perform a simple cross tabulation, i.e., compute a frequency table of the columns user_id and article_id
    
    user_item = pd.crosstab(df['user_id'], df['article_id'])
    
    # return the user_item matrix 
    
    return user_item 

user_item = create_user_item_matrix(df)


## Tests: You should just need to run this cell.  Don't change the code.
assert user_item.shape[0] == 5149, "Oops!  The number of users in the user-article matrix doesn't look right."
assert user_item.shape[1] == 714, "Oops!  The number of articles in the user-article matrix doesn't look right."
assert user_item.sum(axis=1)[1] == 36, "Oops!  The number of articles seen by user 1 doesn't look right."
print("You have passed our quick tests!  Please proceed!")

You have passed our quick tests!  Please proceed!


def find_similar_users(user_id, user_item=user_item):
    '''
    INPUT:
    user_id - (int) a user_id
    user_item - (pandas dataframe) matrix of users by articles: 
                1's when a user has interacted with an article, 0 otherwise
    
    OUTPUT:
    similar_users - (list) an ordered list where the closest users (largest dot product users)
                    are listed first
    
    Description:
    Computes the similarity of every pair of users based on the dot product
    Returns an ordered
    
    '''
    
    ## Compute similarity of each user to the provided user. This involves the following steps.
    
    # Get the data associated with the user whose id is `user_id`. 
    # Note that this encodes the articles that the user has interracted with.
    
    user_data = user_item[user_item.index==user_id].values
    
    
    # Take the dot product of the data associated with the user whose id is `user_id`, i.e., `user_data`, 
    # with all the users in the dataframe `user_item`. Note that `user_item`.values is encoded as a matrix 
    # with number of users as rows and number of articles as column. Hence, taking the transponse will 
    # will ensure the dot product of the data associated with `user_id` with all the other users.
    
    user_data_dot_all_users = np.dot(user_data, user_item.values.T)[0]
    

    ## sort by similarity
    
    # Get index of the dataframe object `user_item`
    
    users_indexes = user_item.index.values
    
    
    # Form a dataframe with the `user_data_dot_all_users`. Note that the positions of the entries of 
    # `user_data_dot_all_users` corresponds to that of `users_indexes`. In effect the similarity of the 
    # user whose id is `user_id` to the i-th user, namely, `user_indexes[i]` is given by 
    # `user_data_dot_all_users[i]`. 
    
    user_users_similarity_df = pd.DataFrame(user_data_dot_all_users, index=users_indexes, columns=['similarity'])
    
    
    # Sort the similarities of the users in increasing order 
    
    user_users_similarity_df.sort_values(by=['similarity'], ascending=False, inplace=True)
    
   
    ## remove the own user's id, that is, remove `user_id` from the index of the dataframe `user_users_similarity_df`.
    
    user_users_similarity_df.drop(labels=[user_id], inplace=True)
    
    
    # Get the indexes of the dataframe `user_users_similarity_df`. Note that these are all other users in the dataframe 
    # `user_item` except for the user whose id is `user_id`. 
    
    most_similar_users = user_users_similarity_df.index.to_list()
    
    
    # Return a list of the users in order from most to least similar
    
    return most_similar_users


# Do a spot check of your function
print("The 10 most similar users to user 1 are: {}".format(find_similar_users(1)[:10]))
print("The 5 most similar users to user 3933 are: {}".format(find_similar_users(3933)[:5]))
print("The 3 most similar users to user 46 are: {}".format(find_similar_users(46)[:3]))

The 10 most similar users to user 1 are: [3933, 23, 3782, 203, 4459, 3870, 131, 4201, 46, 5041]
The 5 most similar users to user 3933 are: [1, 23, 3782, 203, 4459]
The 3 most similar users to user 46 are: [4201, 3782, 23]


def get_article_names(article_ids, df=df):
    '''
    INPUT:
    article_ids - (list) a list of article ids
    df - (pandas dataframe) df as defined at the top of the notebook
    
    OUTPUT:
    article_names - (list) a list of article names associated with the list of article ids 
                    (this is identified by the title column)
    '''
    
    # Convert article_ids to Python float object
    article_ids = list(map(float, article_ids))
    
    # Create a dataframe from df with the article_id's and title not repeated 
    unique_articles_df = df.drop_duplicates(subset='article_id')[['article_id', 'title']].set_index('article_id')
    
    # Get the title of the each article_id in unique_articles_df 
    article_names = unique_articles_df.loc[article_ids]['title'].tolist()
    
    # Return the article names associated with list of article ids
    return article_names 


def get_user_articles(user_id, user_item=user_item):
    '''
    INPUT:
    user_id - (int) a user id
    user_item - (pandas dataframe) matrix of users by articles: 
                1's when a user has interacted with an article, 0 otherwise
    
    OUTPUT:
    article_ids - (list) a list of the article ids seen by the user
    article_names - (list) a list of article names associated with the list of article ids 
                    (this is identified by the doc_full_name column in df_content)
    
    Description:
    Provides a list of the article_ids and article titles that have been seen by a user
    '''
    
    # Get the data associated with the user whose id is `user_id`
    user_data = user_item.loc[user_id]
    
    # Get the indexes which indicates that `user_id` has interacted with an article.
    user_art_inter_index = np.where(user_data == 1)
    
    # Get the `article_id`'s that user_id has interacted with
    user_article_ids = user_data.index[user_art_inter_index].values
    
    # Get the titles of the articles that user_id has interacted with
    article_names = get_article_names(user_article_ids)
    
    # Return the article ids and and their names
    return user_article_ids, article_names 


def user_user_recs(user_id, m=10):
    '''
    INPUT:
    user_id - (int) a user id
    m - (int) the number of recommendations you want for the user
    
    OUTPUT:
    recs - (list) a list of recommendations for the user
    
    Description:
    Loops through the users based on closeness to the input user_id
    For each user - finds articles the user hasn't seen before and provides them as recs
    Does this until m recommendations are found
    
    Notes:
    Users who are the same closeness are chosen arbitrarily as the 'next' user
    
    For the user where the number of recommended articles starts below m 
    and ends exceeding m, the last items are chosen arbitrarily
    
    '''
    # recs is expected to contain at most the first `m`-article_id's to be recommended for user_id
    recs = []
    
    # Get the list of users with similar article interaction as the user whose id is `user_id`
    similar_users = find_similar_users(user_id)
    
    # Get the article_id and the article names that user_id has interacted with
    user_articles_ids, user_articles_names = get_user_articles(user_id)
    
    # For each user in similar_users
    for similar_user in similar_users:
        
        # Get the article_id and the article names that similar_user has interacted with.
        similar_user_article_ids, similar_user_article_names = get_user_articles(similar_user)
        
        # Get the articles that have not been interacted by user_id
        rec_article_ids = set(similar_user_article_ids).difference(set(user_articles_ids))
        
        rec_article_ids = rec_article_ids.difference(set(recs))
        
        # Update recs with `rec_article_ids`
        recs.extend(list(rec_article_ids))
        
        # If the number of `article_id`'s in `recs` is at least `m`
        
        if len(recs) >= m:
            
            break
            
    # Set recs to the `m`-elements in `recs`
    
    recs = recs[:m]
    
    # return the `article_id`'s to be recommended for this user_id
    
    return recs


# Check Results
get_article_names(user_user_recs(1, 10)) # Return 10 recommendations for user 1

['data tidying in data science experience',
 'this week in data science (april 18, 2017)',
 'shaping data with ibm data refinery',
 'fertility rate by country in total births per woman',
 'data science platforms are on the rise and ibm is leading the way',
 'timeseries data analysis of iot events by using jupyter notebook',
 'got zip code data? prep it for analytics. – ibm watson data lab – medium',
 'higher-order logistic regression for large datasets',
 'from scikit-learn model to cloud with wml client',
 'from spark ml model to online scoring with scala']


# Test your functions here
assert set(get_article_names(['1024.0', '1176.0', '1305.0', '1314.0', '1422.0', '1427.0'])) == set(['using deep learning to reconstruct high-resolution audio', 'build a python app on the streaming analytics service', 'gosales transactions for naive bayes model', 'healthcare python streaming application demo', 'use r dataframes & ibm watson natural language understanding', 'use xgboost, scikit-learn & ibm watson machine learning apis']), "Oops! Your the get_article_names function doesn't work quite how we expect."
assert set(get_article_names(['1320.0', '232.0', '844.0'])) == set(['housing (2015): united states demographic measures','self-service data preparation with ibm data refinery','use the cloudant-spark connector in python notebook']), "Oops! Your the get_article_names function doesn't work quite how we expect."
assert set(get_user_articles(20)[0]) == set(list(map(float, ['1320.0', '232.0', '844.0'])))
assert set(get_user_articles(20)[1]) == set(['housing (2015): united states demographic measures', 'self-service data preparation with ibm data refinery','use the cloudant-spark connector in python notebook'])
assert set(get_user_articles(2)[0]) == set(list(map(float, ['1024.0', '1176.0', '1305.0', '1314.0', '1422.0', '1427.0'])))
assert set(get_user_articles(2)[1]) == set(['using deep learning to reconstruct high-resolution audio', 'build a python app on the streaming analytics service', 'gosales transactions for naive bayes model', 'healthcare python streaming application demo', 'use r dataframes & ibm watson natural language understanding', 'use xgboost, scikit-learn & ibm watson machine learning apis'])
print("If this is all you see, you passed all of our tests!  Nice job!")

If this is all you see, you passed all of our tests!  Nice job!


def get_top_sorted_users(user_id, df=df, user_item=user_item):
    '''
    INPUT:
    user_id - (int)
    df - (pandas dataframe) df as defined at the top of the notebook 
    user_item - (pandas dataframe) matrix of users by articles: 
            1's when a user has interacted with an article, 0 otherwise
    
            
    OUTPUT:
    neighbors_df - (pandas dataframe) a dataframe with:
                    neighbor_id - is a neighbor user_id
                    similarity - measure of the similarity of each user to the provided user_id
                    num_interactions - the number of articles viewed by the user - if a u
                    
    Other Details - sort the neighbors_df by the similarity and then by number of interactions where 
                    highest of each is higher in the dataframe
     
    '''
    # Your code here
    
    ## Compute similarity of each user to the provided user. This involves the following steps.
    
    # Get the data associated with the user whose id is `user_id`. 
    # Note that this encodes the articles that the user has interracted with.
    
    user_data = user_item[user_item.index==user_id].values
    
    
    # Take the dot product of the data associated with the user whose id is `user_id`, i.e., `user_data`, 
    # with all the users in the dataframe `user_item`. Note that `user_item`.values is encoded as a matrix 
    # with number of users as rows and number of articles as column. Hence, taking the transponse will 
    # will ensure the dot product of the data associated with `user_id` with all the other users.
    
    user_data_dot_all_users = np.dot(user_data, user_item.values.T)[0]
    
    ## sort by similarity
    
    # Get index of the dataframe object `user_item`
    
    users_indexes = user_item.index.values
    
    # Form a Series object with the `user_data_dot_all_users`. Note that the positions of the entries of 
    # `user_data_dot_all_users` corresponds to that of `users_indexes`. In effect the similarity of the 
    # user whose id is `user_id` to the i-th user, namely, `user_indexes[i]` is given by 
    # `user_data_dot_all_users[i]`.
    
    user_users_similarity = pd.Series(user_data_dot_all_users, index=users_indexes)
    
    # Get a count of the articles that each user has interacted with. 
    
    users_articles_intertns = df.groupby(['user_id'])['article_id'].count()
    
    # Take only those the users in `users_indexes` 
    users_articles_intertns = users_articles_intertns.loc[users_indexes]
    
    # Form a dataframe with users_indexes as index and user_users_similarity and users_articles_interactions
    # as columns.
    neighbors_df = pd.DataFrame({'neighbor_id': users_indexes, 'similarity': user_users_similarity, 
                                 'num_interactions': users_articles_intertns}).set_index('neighbor_id')
    
    
    # Remove the data that correspond to the `user_id` from the dataframe `neighbors_df`.
    neighbors_df.drop(labels=[user_id], inplace=True)
    
    # Sort the columns of `neighbors_df` in descending order.
    
    neighbors_df.sort_values(by=['similarity', 'num_interactions'], ascending=[False, False], inplace=True)
    
    # Return the dataframe specified in the doc_string
    return neighbors_df 


def user_user_recs_part2(user_id, m=10):
    '''
    INPUT:
    user_id - (int) a user id
    m - (int) the number of recommendations you want for the user
    
    OUTPUT:
    recs - (list) a list of recommendations for the user by article id
    rec_names - (list) a list of recommendations for the user by article title
    
    Description:
    Loops through the users based on closeness to the input user_id
    For each user - finds articles the user hasn't seen before and provides them as recs
    Does this until m recommendations are found
    
    Notes:
    * Choose the users that have the most total article interactions 
    before choosing those with fewer article interactions.

    * Choose articles with the articles with the most total interactions 
    before choosing those with fewer total interactions. 
   
    '''
    
    try:
        # recs is expected to contain at most the first `m`-article_id's to be recommended for user_id
        recs = []
    
        # Get the users similar to `user_id` 
        top_users_df = get_top_sorted_users(user_id)
    
        # Get the id of the users similar to `user_id`
        similar_users = top_users_df.index.values
    
        # Get the article_id and the article names that user_id has interacted with
        user_articles_ids, user_articles_names = get_user_articles(user_id)
    
        # Get the count of the articles-user-interactions
        articles_users_intertns = df.groupby(['article_id'])['user_id'].count()
    
        # For each user in similar_users
        for similar_user in similar_users:
        
            # Get the article_id and the article names that similar_user has interacted with.
            similar_user_article_ids, similar_user_article_names = get_user_articles(similar_user)
        
            # Get the articles that have not been interacted by user_id
            rec_article_ids = set(similar_user_article_ids).difference(set(user_articles_ids))
        
            # sort recommended articles by amount of interaction
            rec_article_ids_sorted = articles_users_intertns.loc[list(rec_article_ids)].sort_values(ascending=False).index.to_list()
        
            # Update recs with `rec_article_ids_sorted`
            recs.extend(list(rec_article_ids_sorted))
        
            # If the number of `article_id`'s in `recs` is at least `m`
        
            if len(recs) >= m:
            
                break
            
        # Set recs to the `m`-elements in `recs`
    
        recs = recs[:m]
    
        rec_names = get_article_names(recs)
        
    except:
        
        recs = get_top_article_ids(10, df=df)
        rec_names = get_top_articles(10, df=df)
    
    return recs, rec_names


# Quick spot check - don't change this code - just use it to test your functions
rec_ids, rec_names = user_user_recs_part2(20, 10)
print("The top 10 recommendations for user 20 are the following article ids:")
print(rec_ids)
print()
print("The top 10 recommendations for user 20 are the following article names:")
print(rec_names)

The top 10 recommendations for user 20 are the following article ids:
[1330.0, 1427.0, 1364.0, 1170.0, 1162.0, 1304.0, 1351.0, 1160.0, 1354.0, 1368.0]

The top 10 recommendations for user 20 are the following article names:
['insights from new york car accident reports', 'use xgboost, scikit-learn & ibm watson machine learning apis', 'predicting churn with the spss random tree algorithm', 'apache spark lab, part 1: basic concepts', 'analyze energy consumption in buildings', 'gosales transactions for logistic regression model', 'model bike sharing data with spss', 'analyze accident reports on amazon emr spark', 'movie recommender system with spark machine learning', 'putting a human face on machine learning']


# Find the user that is most similar to user 1 
user1_most_sim = get_top_sorted_users(1).head(1).index[0] 

# Find the 10th most similar user to user 131
user131_10th_sim = get_top_sorted_users(131).head(10).index[-1]


assert user1_most_sim == 3933
assert user131_10th_sim == 242
print("If this is all you see, you passed all of our tests!  Nice job!")

If this is all you see, you passed all of our tests!  Nice job!


new_user = 0.0

recs_article_ids, rec_article_names = user_user_recs_part2(new_user, 10)

print("The top 10 articles that I will recommend for a new user are the following:")
pd.DataFrame({"article_id":recs_article_ids, 
              "article_names": rec_article_names
             })

The top 10 articles that I will recommend for a new user are the following:


# Load the matrix here
user_item_matrix = user_item
user_item_matrix.head()


# The number of zeros in the user_item_matrix data
(user_item_matrix == 0).sum().sum()

3642704


# The number of ones in the user_item_matrix data
(user_item_matrix == 1).sum().sum()

33682


# The user_item_matrix has no missing values 
user_item_matrix.isna().sum().sum()

0


# Perform SVD on the User-Item Matrix Here

u, s, vt = np.linalg.svd(user_item_matrix) # use the built in to get the three matrices


num_latent_feats = np.arange(10,700+10,20)
sum_errs = []

for k in num_latent_feats:
    # restructure with k latent features
    s_new, u_new, vt_new = np.diag(s[:k]), u[:, :k], vt[:k, :]
    
    # take dot product
    user_item_est = np.around(np.dot(np.dot(u_new, s_new), vt_new))
    
    # compute error for each prediction to actual value
    diffs = np.subtract(user_item_matrix, user_item_est)
    
    # total errors and keep track of them
    err = np.sum(np.sum(np.abs(diffs)))
    sum_errs.append(err)
    
## Plotting with the matplotlib library    
#plt.plot(num_latent_feats, 1 - np.array(sum_errs)/df.shape[0]);
#plt.xlabel('Number of Latent Features');
#plt.ylabel('Accuracy');
#plt.title('Accuracy vs. Number of Latent Features');

## Ploting with the plotly library
graph_fig = px.line(x=num_latent_feats, 
                    y=1 - np.array(sum_errs)/df.shape[0], 
                    title="Accuracy vs. Number of Latent Features", 
                    template= "plotly_dark"
                   )

graph_fig.update_layout(title_x=0.5, 
                        xaxis_title = "Number of Latent Features", 
                        yaxis_title = "Accuracy", 
                       )

graph_fig.show(config={'displaylogo': False})


df_train = df.head(40000)
df_test = df.tail(5993)

def create_test_and_train_user_item(df_train, df_test):
    '''
    INPUT:
    df_train - training dataframe
    df_test - test dataframe
    
    OUTPUT:
    user_item_train - a user-item matrix of the training dataframe 
                      (unique users for each row and unique articles for each column)
    user_item_test - a user-item matrix of the testing dataframe 
                    (unique users for each row and unique articles for each column)
    test_idx - all of the test user ids
    test_arts - all of the test article ids
    
    '''
    user_item_train = create_user_item_matrix(df_train)
    user_item_test = create_user_item_matrix(df_test)
    
    test_idx = user_item_test.index.values
    test_arts = user_item_test.columns.values
    
    return user_item_train, user_item_test, test_idx, test_arts

user_item_train, user_item_test, test_idx, test_arts = create_test_and_train_user_item(df_train, df_test)


# Number of users we can predict in the test set
train_idx = user_item_train.index.values
num_test_users_pred = len(np.intersect1d(test_idx, train_idx))
num_test_users_pred

20


# Number of users in the test set that we are not able to make predictions for because of the cold start problem
num_unpred_test_users = len(test_idx) - num_test_users_pred
num_unpred_test_users

662


# Number of articles we can make predictions for in the test set
train_art_idx =  user_item_train.columns.values
test_art_idx =  user_item_test.columns.values

num_test_arts_pred = len(np.intersect1d(test_art_idx, train_art_idx))
num_test_arts_pred

574


# Number of articles in the test set we are not able to make predictions for because of the cold start problem
num_unpred_test_arts = len(test_art_idx) - num_test_arts_pred
num_unpred_test_arts

0


# fit SVD on the user_item_train matrix
u_train, s_train, vt_train = np.linalg.svd(user_item_train) # fit svd similar to above then use the cells below


# Get the dimensions of u_train, s_train, and vt_train
u_train.shape, s_train.shape, vt_train.shape

((4487, 4487), (714,), (714, 714))


# find subset of u_train that are in test_idx
u_test = u_train[user_item_train.index.isin(test_idx),:]
u_test.shape

(20, 4487)


# find subset of vt_train that are in test_arts
vt_test = vt_train[:,user_item_train.columns.isin(test_arts)]
vt_test.shape

(714, 574)


# get subset of user_item_test that we can predict
test_users_w_pred_idx = np.intersect1d(test_idx, train_idx)

#test_users_w_pred_idx
user_item_test_subset = user_item_test[user_item_test.index.isin(test_users_w_pred_idx)]
user_item_test_subset.shape

(20, 574)


num_latent_feats = np.arange(10,700+10,20)

# For accuracy scores
train_accuracy_array = np.array([])
test_accuracy_array = np.array([])

# For F1 scores
train_f1_array = np.array([])
test_f1_array = np.array([])

# For precision scores
train_prec_array = np.array([])
test_prec_array = np.array([])

# For recall scores
train_recall_array = np.array([])
test_recall_array = np.array([])

# for each latent features in `num_latent_feats`
for k in num_latent_feats:
    
    # Take only k latent features for both training and testing data
    new_u_train, new_s_train, new_vt_train = u_train[:,:k], np.diag(s_train[:k]), vt_train[:k,:]
    new_u_test, new_vt_test = u_test[:,:k], vt_test[:k,:]
    
    # Take the dot product of the training and test data to get their predicted values.
    user_item_train_pred = np.around(np.dot(np.dot(new_u_train, new_s_train), new_vt_train))
    user_item_test_pred = np.around(np.dot(np.dot(new_u_test, new_s_train), new_vt_test))
    
    # Set predictions in binary form, i.e., to either 0 and 1. This is because, 
    # we are predicting whether or not a users will interact with articles.
    user_item_train_pred = np.clip(user_item_train_pred, 0, 1)
    user_item_test_pred = np.clip(user_item_test_pred, 0, 1)
    
    # Get accuracy score for the k-th latent feature in num_latent_feats
    accuracy_train = accuracy_score(np.array(user_item_train).flatten(), user_item_train_pred.flatten())
    accuracy_test = accuracy_score(np.array(user_item_test_subset).flatten(), user_item_test_pred.flatten())
    #print("Accuracy Score for Training Data: {}\nAccuracy Score for Test Data: {}\n\n".format(accuracy_train, accuracy_test))
    
    # Get f1 score for for the k-th latent feature in num_latent_feats
    f1_train = f1_score(np.array(user_item_train).flatten(), user_item_train_pred.flatten())
    f1_test = f1_score(np.array(user_item_test_subset).flatten(), user_item_test_pred.flatten())
    #print("F1-Score for Training Data: {}\nF1-Score for Test Data: {}\n\n".format(f1_train, f1_test))
    
    # Get precision score for for the k-th latent feature in num_latent_feats
    prec_train = precision_score(np.array(user_item_train).flatten(), user_item_train_pred.flatten())
    prec_test = precision_score(np.array(user_item_test_subset).flatten(), user_item_test_pred.flatten())
    #print("Precision Score for Training Data: {}\nPrecision Score for Test Data: {}\n\n".format(prec_train, prec_test))
    
    # Get precision score for for the k-th latent feature in num_latent_feats
    recall_train = recall_score(np.array(user_item_train).flatten(), user_item_train_pred.flatten())
    recall_test = recall_score(np.array(user_item_test_subset).flatten(), user_item_test_pred.flatten())
    #print("Recall Score for Training Data: {}\nRecall Score for Test Data: {}\n\n".format(recall_train, recall_test))
    
    # Update `train_accuracy_array` and `test_accuracy_array` with `acc_train` and `acc_test` respectively
    train_accuracy_array = np.append(train_accuracy_array, accuracy_train)
    test_accuracy_array = np.append(test_accuracy_array, accuracy_test)
    
    # Update `train_f1_array` and `test_f1_array` with `f1_train` and `f1_test` respectively
    train_f1_array = np.append(train_f1_array, f1_train)
    test_f1_array = np.append(test_f1_array, f1_test)
    
    # Update `train_prec_array` and `test_prec_array` with `prec_train` and `prec_test` respectively
    train_prec_array = np.append(train_prec_array, prec_train)
    test_prec_array = np.append(test_prec_array, prec_test)
    
    
    # Update `train_recall_array` and `test_recall_array` with `recall_train` and `recall_test` respectively
    train_recall_array = np.append(train_recall_array, recall_train)
    test_recall_array = np.append(test_recall_array, recall_test)


metric_scores_df = pd.DataFrame({"latent_features_num": num_latent_feats, 
                                 "train_accuracy_score" : train_accuracy_array, 
                                 "test_accuracy_score" : test_accuracy_array, 
                                 "train_f1_score" : train_f1_array, 
                                 "test_f1_score" : test_f1_array, 
                                 "train_prec_score" : train_prec_array, 
                                 "test_prec_score" : test_prec_array, 
                                 "train_recall_score" : train_recall_array, 
                                 "test_recall_score" : test_recall_array
                                }).set_index("latent_features_num")
metric_scores_df


def metric_plot(num_latent_feats=num_latent_feats, 
                 metric_train_data=train_accuracy_array, 
                 metric_test_data=test_accuracy_array, 
                 train_data_name="Train Accuracy", 
                 test_data_name="Test Accuracy", 
                 plot_title = "Training and Testing Accuracy Scores verses Number of Latent Features"):
    """
    INPUT:
        - num_latent_feats  - An array of integers of dimention 1-by-k which is the number of latent features.
        - metric_train_data - An array of floats of dimention 1-by-k which is the data obtained from one of the following metrics: 
                            `accuracy_score`, `f1_score`, `precision_score` and `recall_score`.
        - metric_test_data  - An array of floats of dimention 1-by-k which is the data obtained from one of the following metrics: 
                            `accuracy_score`, `f1_score`, `precision_score` and `recall_score`.
        - train_data_name   - A string object which is the name to be used in the legend section of the plot figure for the training
                            data.
        - test_data_name    - A string object which is the name to be used in the legend section of the plot figure for the testing
                            data.
        - plot_title        - A string object which is the title of the plot figure.
    
    OUTPUT:
        - None              - NoneType
    
    """
    # Create figure with secondary y-axis
    fig = make_subplots(specs=[[{"secondary_y": True}]])
    
    
    ## Add traces 
    # For the metric on the training data
    fig.add_trace(
        go.Scatter(x=num_latent_feats, 
                   y=metric_train_data, 
                   name=train_data_name
                  ), 
        secondary_y=False,
    )
    
    # For the metric on the test data
    fig.add_trace(
        go.Scatter(x=num_latent_feats, 
                   y=metric_test_data, 
                   name=test_data_name
                  ),
        secondary_y=True,
    )
    
    # Add figure title
    fig.update_layout(title_x=0.5,
                      title_text=plot_title,
                      template= "plotly_dark"
                     )

    # Set x-axis title
    fig.update_xaxes(title_text="Number of Latent Features")

    # Set y-axes titles
    fig.update_yaxes(title_text="<b>" + train_data_name + "</b>", secondary_y=False)
    fig.update_yaxes(title_text="<b>" + test_data_name + "</b>", secondary_y=True)

    fig.show(config={'displaylogo': False})


# Plotting for accuracy scores and number of latent features
metric_plot()


# Plotting for F1 scores and number of latent features

metric_plot(metric_train_data=train_f1_array, 
            metric_test_data=test_f1_array, 
            train_data_name="Train F1-Score", 
            test_data_name="Test F1-Score", 
            plot_title="Training and Testing F1-Scores verses Number of Latent Features"
           )


# Plotting for precision scores and number of latent features

metric_plot(metric_train_data=train_prec_array, 
            metric_test_data=test_prec_array, 
            train_data_name="Train Precision Score", 
            test_data_name="Test Precision Score", 
            plot_title="Training and Testing Precision Scores verses Number of Latent Features"
           )


# Plotting for recall scores and number of latent features

metric_plot(metric_train_data=train_recall_array, 
            metric_test_data=test_recall_array, 
            train_data_name="Train Recall Score", 
            test_data_name="Test Recall Score", 
            plot_title="Training and Testing Recall Scores verses Number of Latent Features"
           )

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	title	email
article_id
268.0	5	0
20.0	1	0
62.0	1	0
162.0	1	0
224.0	1	0
415.0	1	0
647.0	1	0
846.0	1	0
961.0	1	0
965.0	1	0
1016.0	1	0
1174.0	1	0
1393.0	1	0

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0	1430.0	using pixiedust for fast, flexible, and easier...	ef5f11f77ba020cd36e1105a00ab868bbdbf7fe7
1	1314.0	healthcare python streaming application demo	083cbdfa93c8444beaa4c5f5e0f5f9198e4f9e0b
2	1429.0	use deep learning for image classification	b96a4f2e92d8572034b1e9b28f9ac673765cd074
3	1338.0	ml optimization using cognitive assistant	06485706b34a5c9bf2a0ecdac41daf7e7654ceb7
4	1276.0	deploy your python model as a restful api	f01220c46fc92c6e6b161b1849de11faacd7ccb2

Recommendations with IBM¶

Table of Contents¶

Part I : Exploratory Data Analysis ¶

Part II: Rank-Based Recommendations ¶

Part III: User-User Based Collaborative Filtering ¶

Part IV: Matrix Factorization ¶

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0	0000b6387a0366322d7fbfc6434af145adf7fed1	13
1	001055fc0bb67f71e8fa17002342b256a30254cd	4
2	00148e4911c7e04eeff8def7bbbdaf1c59c2c621	3
3	001a852ecbd6cc12ab77a785efa137b2646505fe	6
4	001fc95b90da5c3cb12c501d201a915e4f093290	2

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user_id
1	0	0	0	0	0	0	0	0	0	0	...	0	0	1	0	1	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	1	0	0	0	0	...	0	0	1	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
5	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

	train_accuracy_score	test_accuracy_score	train_f1_score	test_f1_score	train_prec_score	test_prec_score	train_recall_score	test_recall_score
latent_features_num
10	0.991735	0.978397	0.202458	0.074627	0.853479	0.200000	0.114851	0.045872
30	0.993505	0.976568	0.456642	0.123779	0.968006	0.213483	0.298797	0.087156
50	0.994605	0.975348	0.583418	0.129231	0.989939	0.196262	0.413580	0.096330
70	0.995586	0.973606	0.682032	0.126801	0.997173	0.170543	0.518248	0.100917
90	0.996367	0.972300	0.751860	0.131148	0.999547	0.162162	0.602549	0.110092
110	0.996959	0.970470	0.800352	0.124031	0.999591	0.142012	0.667339	0.110092
130	0.997482	0.969512	0.840130	0.125000	0.999859	0.137363	0.724405	0.114679
150	0.997898	0.968728	0.869968	0.126521	0.999956	0.134715	0.769888	0.119266
170	0.998228	0.968031	0.892564	0.128266	1.000000	0.133005	0.805973	0.123853
190	0.998512	0.967334	0.911330	0.125874	1.000000	0.127962	0.837104	0.123853
210	0.998746	0.966638	0.926308	0.123570	1.000000	0.123288	0.862732	0.123853
230	0.998954	0.966289	0.939287	0.122449	1.000000	0.121076	0.885525	0.123853
250	0.999141	0.965767	0.950660	0.120805	1.000000	0.117904	0.905960	0.123853
270	0.999287	0.965418	0.959373	0.119734	1.000000	0.115880	0.921918	0.123853
290	0.999404	0.965157	0.966302	0.118943	1.000000	0.114407	0.934800	0.123853
310	0.999500	0.964808	0.971840	0.117904	1.000000	0.112500	0.945223	0.123853
330	0.999580	0.964808	0.976443	0.117904	1.000000	0.112500	0.953971	0.123853
350	0.999645	0.964634	0.980189	0.117391	1.000000	0.111570	0.961147	0.123853
370	0.999709	0.964634	0.983836	0.117391	1.000000	0.111570	0.968186	0.123853
390	0.999781	0.964460	0.987843	0.116883	1.000000	0.110656	0.975977	0.123853
410	0.999817	0.964460	0.989886	0.116883	1.000000	0.110656	0.979975	0.123853
430	0.999851	0.964460	0.991766	0.116883	1.000000	0.110656	0.983666	0.123853
450	0.999879	0.964460	0.993344	0.116883	1.000000	0.110656	0.986776	0.123853
470	0.999907	0.964460	0.994865	0.116883	1.000000	0.110656	0.989783	0.123853
490	0.999928	0.964460	0.996038	0.116883	1.000000	0.110656	0.992106	0.123853
510	0.999943	0.964460	0.996863	0.116883	1.000000	0.110656	0.993747	0.123853
530	0.999962	0.964460	0.997911	0.116883	1.000000	0.110656	0.995831	0.123853
550	0.999976	0.964460	0.998683	0.116883	1.000000	0.110656	0.997369	0.123853
570	0.999985	0.964460	0.999196	0.116883	1.000000	0.110656	0.998394	0.123853
590	0.999991	0.964460	0.999521	0.116883	1.000000	0.110656	0.999043	0.123853
610	0.999993	0.964460	0.999624	0.116883	1.000000	0.110656	0.999248	0.123853
630	0.999996	0.964460	0.999761	0.116883	1.000000	0.110656	0.999522	0.123853
650	0.999999	0.964460	0.999949	0.116883	1.000000	0.110656	0.999897	0.123853
670	1.000000	0.964460	1.000000	0.116883	1.000000	0.110656	1.000000	0.123853
690	1.000000	0.964460	1.000000	0.116883	1.000000	0.110656	1.000000	0.123853

Recommendations with IBM¶

Table of Contents¶

Part I : Exploratory Data Analysis¶

Part II: Rank-Based Recommendations¶

Part III: User-User Based Collaborative Filtering¶

Part IV: Matrix Factorization¶

Part I : Exploratory Data Analysis ¶

Part II: Rank-Based Recommendations ¶

Part III: User-User Based Collaborative Filtering ¶

Part IV: Matrix Factorization ¶